基於可重組平台之公共運輸組員資源管理系統

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(1)國立臺灣師範大學工業教育學系碩士論文 Department of Industrial Education National Taiwan Normal University Master Thesis. 指導教授：洪翊軒博士 Advisor: Yi-Hsuan Hong, Ph.D. 基於可重組平台之公共運輸組員資源管理系統 A Configurable Platform Based Crew Resource Management System for Public Transportations. 研究生姓名：劉蔡權. 撰. 英文姓名：Liu, Tsai-Chuan 中華民國 103 年 7 月 July 2014.

(2) Abstracts The Crew Resource Management (CRM) system is an applied logical management system initially developed by civil aviation. The aim of the CRM system development is to minimize the risks being caused by human factors. The CRM concept has been widely deployed in many transportation systems being operated in the environment with risks and uncertainties. Although the CRM systems have been widely adopted in airlines, such systems are still rare in the land and sea based transportation systems. Meanwhile, porting the CRM system for the civic aviation vehicles to the land and sea based systems is one of the most feasible ways to minimize both the time to market and the R&D cost. However, how the CRM systems being used in the airplanes can be ported to the land and sea transportation systems is still to be explored. Therefore, this research aims to adopt the Delphi method with DEMATEL based Network Process (DNP) Technique to summarize opinions being provided by experts in the related fields of army aviation, commercial aviation, high-speed rail, and air rescue from the dimensions of top-down support, teamwork building, situational awareness, and emergency control perspectives. From the survey results, the evaluation criterion will consist of four dimensions and eight elements, including leadership guidance, operation command & control, crew interaction, backup support, environment effects, information sharing, risk management, and crisis response for consideration. Then the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is used to determine the alternatives ranking for a compromise solution. Finally, the platform based design method is used to serve as the basis for establishing a configurable platform based CRM system for all public transportations, based on different requirements of various transportation systems. ii.

(3) From the results of this research, it is found that the final key factors will be selected for the constructing of basic CRM model of high-speed rail transportation system including crew backup support, information sharing, risk management, operation command & control, crisis response, and crew interaction; for sea transportation CRM, there are crew backup support, information sharing, crew interaction, crisis response, and environment effects by sequence; finally, for low elevation operated air carrier (LEOAC) CRM, there are information sharing, crew backup support, crew interaction, and crisis response. Keywords: Crew Resource Management; Human Factors; Risk Management; Backup Support Mechanism.. iii.

(4) 摘要「組員資源管理」(Crew Resource Management, CRM)為源自民航體系為減少人為因素所導致危險的應用管理概念，至今已被視為改善飛航安全，以及促進整體運作效率的一大利器，並推廣至醫療、消防救災、核能發電廠等多種高風險行業。雖民航公司已普遍採用組員資源管理概念，但在陸上及海上公共運輸系統尚無完整的「組員資源管理系統」，如能將民航公司所採用的組員資源管理概念導入至陸上及海上公共運輸系統，不失為可降低行銷時間及研發成本的最為可行方法之一。然而，此種將運用於飛機系統的組員資源管理概念移植至陸上及海上公共運輸系統的作法，仍有待進一步探討。本文即嘗試以德菲法(Delphi Method)以及基於決策實驗室分析法 (Decision Making Trial and Evaluation Laboratory, DEMATEL)之網路流程(DEMATEL based Network Process, DNP)，邀集來自陸軍航空、民航、高速鐵路及空中搜救等單位之資深從業人員擔任專家，從管理階層上對下支援、組員團隊建立、狀況覺知與緊急事件管控等層面著手，確立管理階層決策指導、營運指揮與管制、組員互動、備援機制、環境效應、資訊分享、風險管理與危機應對等八項準則，針對空用、陸用及海用等三種公共運輸系統載台不同之特性與需求進行評估，並運用 VIKOR 方法決定妥協之解決方案。最後，以平台為基礎之設計方法，建立一個基於不同系統需求，可重組平台之組員資源管理模組。經過本研究結果顯示，高速鐵路運輸系統可以組員備援系統、資訊分享、風險管理、行控中心管控作為、危機處理及團隊人際關係互動為基本組員資源管理面向；海運運輸系統可以組員備援系統、資訊分享、團隊人際關係互動、危機處理及周邊環境影響為基本組員資源管理面向；而低空操作航空器運輸系統可以資訊分享、組員備援系統、團隊人際關係互動及危機處理為基本組員資源管理面向。關鍵詞：組員資源管理、人為因素、風險管理、組員備援機制 iv.

(5) Contents Abstracts ................................................................................................................. ii Chapter 1 Introduction ......................................................................................... 1 1.1. Research Background and Motives .......................................................... 1 1.2. Research Purposes .................................................................................... 3 1.3. Research Method ...................................................................................... 3 1.4. Research Scope and Limitation ................................................................ 4 1.5. Expected Results....................................................................................... 5 1.6. Thesis Structure ........................................................................................ 5 Chapter 2 Literature Review ................................................................................ 7 2.1. Public Transportation ................................................................................ 7 2.1.1. Land-based Public Transportation ..................................................... 8 2.1.2. Air Transportation .............................................................................. 9 2.1.3. Sea Transportation ........................................................................... 10 2.1.4. Security and Safety Concerns for Public Transportations ............... 11 2.2. Risk Management in Public Transportations .......................................... 12 2.3. Crew Resource Management .................................................................. 12 2.3.1. The Concept and Definition of Crew Resource Management ......... 13 2.3.2. The Evolution of Crew Resource Management Theories ................ 14 2.4. Platform Based Design ........................................................................... 16. v.

(6) Chapter 3 Research Methodology ...................................................................... 19 3.1. The Analytical Process ........................................................................... 19 3.2. Delphi Method ........................................................................................ 19 3.3. The DEMATEL Method ......................................................................... 20 3.4. The Analytic Network Process (ANP) Method ...................................... 24 3.5. DEMATEL Based Network Process (DNP) Technique ......................... 29 3.6. VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) ..... 34 3.7. Criteria Definitions ................................................................................. 37 3.7.1. CRM ................................................................................................ 37 3.7.2. Public Transportation System .......................................................... 38 Chapter 4 Empirical Study ................................................................................. 40 4.1. Decision Structure Derivations by Delphi Process ................................. 40 4.2. Causal Relationship by DNP Process ..................................................... 45 4.3. High-speed Rail Causal Relationship by DNP Process .......................... 49 4.4. Sea Transport Causal Relationship by DNP Process .............................. 53 4.5. LEOAC Causal Relationship by DNP Process ....................................... 56 4.6. Compromise Ranking by VIKOR for Various Transportations .............. 60 4.6.1. High-speed Rail ............................................................................... 60 4.6.2. Sea Transportation ........................................................................... 62 4.6.3. Air Transportation / LEOAC ........................................................... 64 4.7. Model Development Process .................................................................. 66 vi.

(7) Chapter 5 Discussion ........................................................................................... 68 5.1. A New Look at the CRM Concept .......................................................... 68 5.2. CRM Requirements for Various Transportation Platforms .................... 68 5.3. Relationship between Criteria and Accident Factor on CRM ................ 69 5.4. Basic CRM Model Design ...................................................................... 71 5.5. System Inputs and Outputs ..................................................................... 72 5.5.1. Leadership Guidance ....................................................................... 72 5.5.2. Operation Command and Control.................................................... 73 5.5.3. Crew Interaction .............................................................................. 74 5.5.4. Backup Support ............................................................................... 74 5.5.5. Environment Effects ........................................................................ 75 5.5.6. Information Sharing ......................................................................... 76 5.5.7. Risk Management ............................................................................ 76 5.5.8. Crisis Response................................................................................ 77 5.6. Different CRM Model Designs for Various Platforms ........................... 78 Chapter 6 Conclusion .......................................................................................... 80 References............................................................................................................. 81 Annex 1 List of experts........................................................................................ 89 Annex 2 The survey questionnaire for factors involving CRM ....................... 90. vii.

(8) Tables Table 1: Current Major High-speed Rail Operating Countries................................ 9 Table 2: The Evolution of CRM .......................................................................... 16 Table 3: Criteria Definition for Identifying CRM Factors .................................... 38 Table 4: The First Round of Delphi Process.......................................................... 41 Table 5: The Second Round of Delphi Process ..................................................... 42 Table 6: The Third Round of Delphi Process ........................................................ 43 Table 7: The Fourth Round of Delphi Process ...................................................... 44 Table 8: The Fifth Round of Delphi Process ......................................................... 45 Table 9: The Initial Direct Influence Matrix D...................................................... 46 Table 10: The Normalized Initial Direct Influence Matrix N ............................... 46 Table 11: The Matrix I ........................................................................................... 47 Table 12: Total Influence Matrix T ....................................................................... 47 Table 13: X and Y Distribution Table ................................................................... 48 Table 14: The Weights versus Each Criteria by DNP ........................................... 49 Table 15: The Initial Direct Influence Matrix D.................................................... 50 Table 16: The Normalized Initial Direct Influence Matrix N ................................ 50 Table 17: The Matrix I........................................................................................... 50 Table 18: The Total Influence Matrix T ................................................................ 51 Table 19: X and Y Distribution Table .................................................................... 51. viii.

(9) Table 20: The Weights versus Each Criteria by DNP............................................ 52 Table 21: The Initial Direct Influence Matrix D.................................................... 53 Table 22: The Normalized Initial Direct Influence Matrix N ................................ 53 Table 23: The Matrix I........................................................................................... 54 Table 24: The Total Influence Matrix T ................................................................ 54 Table 25: X and Y Distribution Table .................................................................... 55 Table 26: The Weights versus Each Criteria by DNP............................................ 56 Table 27: The Initial Direct Influence Matrix D.................................................... 57 Table 28: The Normalized Initial Direct Influence Matrix N ................................ 57 Table 29: The Matrix I........................................................................................... 58 Table 30: The Total Influence Matrix T ................................................................ 58 Table 31: X and Y Distribution Table .................................................................... 59 Table 32: The Weights versus Each Criteria by DNP............................................ 60 Table 33: The Average Scores for High-speed Rail .............................................. 61 Table 34: VIKOR versus Weighted Average Results for High-speed Rail ........... 61 Table 35: The Average Scores for Sea Transportation .......................................... 62 Table 36: VIKOR versus Weighted Average Results for Sea Transportation ....... 63 Table 37: The Average Scores for LEOAC ........................................................... 64 Table 38: VIKOR versus Weighted Average Results for LEOAC ........................ 65 Table 39: CRM Model Development Process.. .................................................. ...67 Table 40: Different CRM Requirements for Various Transportations ................... 68 ix.

(10) Figures Figure 1: Research Structure ................................................................................. 6 Figure 2: Analytical Framework ......................................................................... 19 Figure 3: An Example of the Directed Graph ...................................................... 22 Figure 4: The Control Hierarchy ......................................................................... 27 Figure 5: Connections in a Network .................................................................... 27 Figure 6: DNP Process ........................................................................................ 30 Figure 7: The Causal Diagram of Criteria Relationship ...................................... 48 Figure 8: The Causal Diagram of Criteria Relationship for High-speed Rail ..... 52 Figure 9: The Causal Diagram of Criteria Relationship for Sea Transportation . 55 Figure 10: The Causal Diagram of Criteria Relationship for LEOAC ................ 59 Figure 11: V Value for High-speed Rail .............................................................. 62 Figure 12: V Value for Sea Transportation .......................................................... 64 Figure 13: V Value for LEOAC ........................................................................... 66 Figure 14: Relationship between Criteria and Accident Factor on High-speed Rail ... 69 Figure 15: Relationship between Criteria and Accident Factor on Sea Transportation ... 70 Figure 16: Relationship between Criteria and Accident Factor on LEOAC ....... 70 Figure 17: Basic CRM Model Design ................................................................. 71 Figure 18: System Functions for CRM model .................................................... 72 Figure 19: System Inputs and Outputs for Leadership Guidance Process .......... 73. x.

(11) Figure 20: System Inputs and Outputs for Operation Command and Control Process 73 Figure 21: System Inputs and Outputs for Crew Interaction Process .................. 74 Figure 22: System Inputs and Outputs for Backup Support Process ................... 75 Figure 23: System Inputs and Outputs for Environment Effects Process ........... 75 Figure 24: System Inputs and Outputs for Information Sharing Process ............ 76 Figure 25: System Inputs and Outputs for Risk Management Process ............... 77 Figure 26: System Inputs and Outputs for Crisis Response Process ................... 77 Figure 27: Basic Model for High-speed Rail CRM ............................................. 78 Figure 28: Basic Model for Sea Transport CRM ................................................. 78 Figure 29: Basic Model for LEOAC CRM ......................................................... 79. xi.

(12) Chapter 1 Introduction 1.1. Research Background and Motives Human errors are the actions or interactions which lead to the deviations from crew’s intentions or situational awareness. Human errors in the operations tend to reduce the margin of safety limitations and increase the possibility of accidents or incidents (Kanki, Helmreich & Anca, 2010). Management policies, regulations, and standard operation procedures (SOP) are required to correct human errors.. The CRM (Crew Resource Management) is a “human factor” approach for improving the safety of transportation systems by preventing or managing human errors. Even though the air and land-based transportation systems have been recognized as safe modes of public transportation in the world given today’s technological development, human performance involved in the operating of those travel modes is something that cannot be ignored. Human factors approach is a multifaceted effort to develop information about human capabilities and limitation and to apply this information to manage personnel, equipment, systems, procedures, environment, and training (US GAO, 1997). Under this approach, crews are trained to recognize the potential risk and danger in the operations to prevent accidents and incidents. The Crew Resource Management (CRM) was initially developed by US aviation industry in the 1970s to reduce air crashes caused by human errors. Researches in the US had found that the primary cause of the majority of flight accidents was human error caused by communication failure, poor leadership, and poor decision making in the cockpit. According to the study, the first accident used to describe the requirement for CRM training was from the investigation of the 1972 Eastern Airlines crash of an 1.

(13) L-110 airliner in the Florida Swamps. In this case, nothing had been wrong with the jet except that a light bulb had burned out. It went down because the whole crew were preoccupied, to a man, with trying to see if the landing gear position indicator had failed or not. The plane slowly descended into the swamps and killed most of the passengers and crew (Randall, 2013). Over a period of years, the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) had therefore defined a requirement to implement training for flight crews in the management of cockpit resources to help prevent such accidents. The CRM was then originally named as the Cockpit Resource Management. After several decades of development and application, it has now been regarded as a great tool to improve flight safety. Today, the CRM technology has been spilled over to sectors other than the civic aviation industry. Although the CRM systems have been widely adopted in airlines, such systems are still rare in the land and sea transportation systems. Meanwhile, porting the CRM system for the civic aviation vehicles to the land and sea based systems is one of the most feasible ways to minimize both the time to market and the R&D cost. However, how the CRM systems being used in the airplanes can be ported to the land and sea based transportation systems is still to be explored. In this research, the concept of product design and development method is employed to establish a configurable platform based model of CRM system for public transportations which is able to be modified or arranged differently to adapt for some specific purpose or particular application based on the various platforms used by the public transportations.. 2.

(14) 1.2. Research Purposes This research aims to adopt the Delphi with DEMATEL and ANP methods to accomplish the following purposes: (1) Exploring the nature and risks involved in airline business and land and sea based public transportations; (2) Reviewing the business management from crew’s bottom up perspectives; (3) Utilizing the concept and theory of CRM to survey the management and decision making models of civil aviation with land and sea based public transportation systems; (4) Looking into the requirements for a workable CRM system with reference to the CRM policies established by the civil aviation authorities; (5) Constructing an optimum structure for enhancing overall operational safety and effectiveness.. 1.3 Research Method This research first uses the Delphi method to summarize opinions being provided by experts in the related fields of high-speed rail, commercial aviation, and search and rescue from the aspects of the system operations, technology proficiency, managerial decision making, operation control center’s command and control, crew education and training, work environment familiarity, interpersonal relationship, environmental factors, operation information usage, as well as risk management and crisis response. Then it combines the use of the Decision Making Trial and Evaluation Laboratory (DEMATEL) based Network Process (DNP) methods to solve the cause and effect relationship among the evaluation criteria and to derive interrelationship among factors. Next, it uses the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to determine the alternatives ranking for a compromise solution. After identifying the key factors involved and their cause and effect relationship, a model of the CRM system can be established by applying product design and 3.

(15) development method. The CRM model can be explored from top-down support, teamwork building, situational awareness, and emergency control perspectives and must include the management level leadership guidance and operation command and control on the top. Crew interaction and backup support mechanism need to be in place to ensure a strong work team can be created. Surrounding environment’s effects and operational information sharing must be integrated horizontally to provide situational awareness. Finally, emergency control and handling mechanism including risk management and crisis response must be in place to ensure overall operational efficiency and safety. The analytic results can serve as the basis for establishing a configurable platform based CRM system for all public transportation systems.. 1.4. Research Scope and Limitation This research will look into the operations of civil aviation with land and sea based public transportation systems in the related fields of system operations, technology proficiency, managerial decision making, operation control center’s command and control, crew education and training, work environment familiarity, interpersonal relationship, environmental factors, operation information usage, as well as risk management and crisis response. However, due to the author’s personal work experiences and invited experts’ knowledge, this research will have the following limitations: (1) this research will be based on the operations of civil transportation systems only; (2) due to different culture, the opinions will be derived from Taiwanese experts; (3) the verifications of the models will be based on empirical studies of civic transportation systems. This, the results can be controversial.. 4.

(16) 1.5. Expected Results The following research results can be expected: (1) an understanding of the nature and risks being involved in the operation and management process of traffic systems by both the managerial level and the operational crew; (2) shaping the business culture from bottom up review to better appreciate the importance of operational safety; (3) rebalancing the operational efficiency and effectiveness with safety concerns; (4) constructing and implementing a configurable CRM platform to enhance the overall operational safety and effectiveness for both air and land-based public transportation systems.. 1.6. Thesis Structure This research begins with the research motive and purpose and uses the cases of civil aviation and high-speed rail operations to explore the feasibility of establishing a CRM system model for all public transportation systems. Next, the literature will be reviewed to look into the nature and characteristics of public transportation. Then the concept of the CRM and the evolution will be summarized. In order to have better understanding of the criteria that must be taken into consideration when constructing the CRM system, the Delphi Method was introduced based on the opinions being derived from eleven Taiwanese experts being employed in military and civil aviation, high-speed rail. The experts are with a minimum of five-year work experiences. After identifying the criteria, the DNP was introduced to derive the causal relationship as well as weights being associated with the criteria. Finally, based on the analytic results, a configurable CRM platform was constructed and verified. The research structure is as follows (Figure 1).. 5.

(17) Figure 1: Research Structure. 6.

(18) Chapter 2 Literature Review In order to better identify the key factors and their relationship for establishing a workable CRM system, this research goes through the literature review from the definition of public transportation and its characteristics, concept and evolution of CRM theories, and platform based design methodology.. 2.1. Public Transportation The United States defines the term “public transportation” in the US Federal Transit Act, 49 U.S. Code § 5302 (a) (10), as transportation by a conveyance that provides regular and continuing general or special transportation to the public, but does not include school bus, charter, sightseeing, or intercity bus transportation or intercity passenger rail transportation provided by such entity as Amtrak or its successor, described in 49 U.S. Code Chapter 243. The ROC Ministry of Transportation and Communications (MOTC) defines the “public transportation” in the Act of Encouraging Public Transportation Development, Article 2, as “the common transportation with fixed routes, schedules, terminals, and fare rates, and providing passenger travelling service.” The public transportation enterprises governed by the Act include the public or private enterprises established in accordance with applicable laws and regulations, and which provide domestic passenger travelling service such as: (1) Urban Bus Carriers; (2) Highway Bus Carriers; (3) Railway Transport Industry; (4) Mass Rapid Transit; (5) Shipping Carriers; (6) Ferry carriers; and (7) Civil Air Transport Enterprises. Due to author’s personal work experiences and invited experts’ knowledge, this research will focus on the air and land-based public transportation systems for discussion. According to the above definitions, public transportation is a shared and open 7.

(19) passenger transportation service which is available for use by the general public and has the following characteristics: (1) shared and open to the general public, (2) fixed routes or navigation lines, (3) fixed schedules or timetables, (4) passenger terminals, (5) fare-based rates.. 2.1.1. Land-based Public Transportation Land-based public transportation system refers to those transport modes running on land or underground which can also be divided into two categories: one for traveling by rail, such as mass rapid transit system (MRT), high-speed rail and conventional rail; and the others for traveling by road, such as buses or Bus Rapid Transit (BRT). Taxicabs, carpooling and hired buses are excluded because they are not shared by strangers without private arrangement. High-speed rail is a new type of rail transport that travels much faster than traditional rail traffic. Defined in the Section 26105 of US Federal Code Title 49 Transportation and the Article 2 of ROC Railway Operational Regulation, high-speed rail refers to trains that travel on special tracks at over 200 km/h. The European Union Directive 96/48/EC, Annex 1, uses a broader definition to encompass a large number of systems under the name of high speed. It defines high-speed rail as a set of three elements with following criteria: (1) infrastructure: track built specially for high-speed travel or specially upgraded for high-speed travel; (2) speed limit: a minimum speed of 250 km/h (155 mph) on lines specially built for high speed and 200 km/h (124 mph) on existing lines which have been specially upgraded. This must apply to at least one section of the line. Rolling stock must have a maximum speed of at least 200 km/h to be considered high speed; (3) operating conditions: rolling stock must be designed alongside its infrastructure for complete compatibility, safety and quality of service.. 8.

(20) The high-speed rail development was first initiated in Germany in 1899 (Sastrasinh, 2000) and has currently been in operation in more than 20 countries while there are more than 24 countries are under construction or development. The major High-speed rail operated countries worldwide are listed below: Table 1: Current Major High-speed Rail Operating Countries. Country. First Operation Year. Max Operating Speed. Japan. 1964. 320km/hr. Taiwan. 2007. 300km/hr. France. 1981. 320km/hr. China. 2003. 350km/hr. Belgium. 1997. 300km/hr. Germany. 1899. 300km/hr. Italy. 1938. 300km/hr. South Korea. 2004. 300km/hr. United Kingdom. 1976. 300km/hr. U.S.A. 2001. 240km/hr. Source: International Union of Railways (UIC), 2013. High-speed rail is probably one of the greatest forms of transportation ever invented on land by delivering fast and efficient mobility to the people every day. The advantages of high-speed rails are becoming clear since they even force airlines to suspend flight services among major cities (Guo, 2011). However, its great speed could also mean disaster to the public given the several significant tragic accidents being happened around the world which deserves further attention for its safety prevention.. 2.1.2. Air Transportation It has always been human’s dream to fly over the sky. Since the first full-size 9.

(21) man-carrying flying model was successfully tested in the 19th century and the Wright brothers made their first successful flight in 1903, humans have seen the fast development of aircraft. Then the first scheduled airline flight was conducted in 1914. The golden age of aviation had arrived and civil aviation became popular. However, a series of mid-air collisions and crashes had sparked the public attention and underlined the need for more regulation of the aviation industry (US Department of Transportation, 2008). Given its complexity and purpose of flight, aviation is no longer conducted by one person alone. The composition of the crew may include the pilots in the cockpit, commanded by flight captain, and extend to the flight attendants and engineers onboard. From broader sense, it may also include the flight dispatchers, aviation controllers, and maintenance personnel on the ground. Therefore, it becomes more and more important to bring these different groups of personnel together to make sure the aviation is conducted in a safe and efficient manner.. 2.1.3. Sea Transportation Sea Transportation is also one mode of public transportation. It usually refers to any type of item being transported over a body of water aboard a boat, ship, or other vessel. Sea transportation has been the largest carrier of freight in the history. Although the importance of sea travel for passengers has been decreasing due to the development of aviation, it is still a popular means for commerce, short trips and pleasure cruises. Furthermore, transport by water is cheaper than transport by air, and it can be through any distance over oceans and lakes or along rivers (Weintrit & Neumann, 2013). More complex than air transportation in the composition of crew, sea transportation takes more personnel to operate the ships to make them function 10.

(22) efficiently. According to International Convention on Standards of Training, Certification and Watchkeeping for Seafarers, 1978, as amended, and related maritime laws, a modern commercial ship’s crew can generally be divided into four main categories: the deck department, the engineering department, the steward’s department, and others. Therefore, the crew’s training, certification, and management are important subjects to keep them together and function as a team.. 2.1.4 Security and Safety Concerns for Public Transportations Public transportation has now become the most convenient and economical way to travel between the cities and even around the world. It moves large amount of people around every day and becomes an important part of people’s daily life, and also a major component of the economy (Polzin, 2002). However, the operation of public transportation is so complex that its safety and security is a big concern for a nation. Though the majority of modern public transportation systems is well designed and has lower accident rates, some occasional highly publicized accidents or incidents can draw widespread concerns. Concerns for personal safety and security of systems may normally affect many people’s decision to use public transportation. Various factors contribute to this excessive fear, including the nature of public transit travel, heavy media coverage of transportation-related crashes and incidents, and conventional traffic safety messages which emphasize danger rather than safety (Litman, 2014). Due to the frequency of use by the public and the amount of people involved, any form of public transportation accident may have a huge impact on the lives of victims and their families. How to respond to the nature disasters and those unexpected events to ensure safety for all passengers under emergency situation has become critical and should be considered seriously.. 11.

(23) 2.2. Risk Management in Public Transportations With the frequency of use by the public and the amount of people involved, the crews of public transportations normally have to work in highly dynamic environments which include elements of danger and uncertainty. Also, they are frequently forced to make decisions when face with danger and crisis (LeSage, Dyar & Evans, 2011). In the international standard ISO 31000, risk is defined as “the effects of uncertainty on objectives.” Risk could be anything that becomes an obstacle to achieving goals and objectives while risk management is a process of analytical and management activities that focus on identifying and responding to the inherent uncertainties (Curtis, et al., 2012). In public transportations, risk usually includes two components: the likelihood of a threat and the severity of its potential consequences (Kanki, Helmreich & Anca, 2010). Although the events with severe consequences do not occur frequently, they are the ones that deserve major attention whenever the transportation systems are in operations. In the case of air transportation, these major events include engine failure, fires and other emergencies. Other threats that are likely to happen include unfavorable traffic conditions, weather, and schedule delays. Proper risk identification, assessment, and preventive measures are critical for risk management.. 2.3. Crew Resource Management Human errors are the actions or interactions which lead to the deviations from crew’s intentions or situational awareness. Human errors in the operations tend to reduce the margin of safety limitations and increase the possibility of accidents or incidents (Kanki, Helmreich & Anca, 2010). Management policies, regulations, and standard operation procedures (SOP) are required to correct human errors. 12.

(24) The CRM is a “human factor” approach for improving the safety of transportation systems by preventing or managing human errors. Even though the air and land-based transportation systems have been recognized as safe modes of public transportation in the world given today’s technological development, human performance involved in the operating of those travel modes is something that cannot be ignored. Human factors approach is a multifaceted effort to develop information about human capabilities and limitation and to apply this information to manage personnel, equipment, systems, procedures, environment, and training (US GAO, 1997). Under this approach, crews are trained to recognize the potential risk and danger in the operations to prevent accidents and incidents.. 2.3.1. The Concept and Definition of CRM The CRM began in the aviation industry in the late 1970s following a string of serious aviation accidents precipitated by the ineffective safety management of available resources. CRM can be defined as the use of all available resources, including equipment, procedures and people, to promote safety and enhance the efficiency of flight operations (Randall, 2013). It is the effective use of all resources to minimize human error, improve operation performance, and reduce the commercial aviation flying operation cost. CRM has now been in existence for over three decades within the aviation industry as hazard management of the flying operation. CRM encompasses. a wide. range of knowledge, skills. and attitudes. including. communications, situational awareness, problem solving, decision making and teamwork, together with all the attendant sub-disciplines in which each of these areas entails, while CRM training is a form of applied logical human factors training to provide operational personnel including any flying crew members with the knowledge, skills and attitudes to manage themselves and available resources to operate more 13.

(25) safely and effectively (SRG, 2006). The elements which comprise CRM are not new but have been recognized in one form or another since aviation began, usually under more general headings such as airmanship, leadership, crew cooperation, etc. According to US Federal Railroad Administration’s research, the proven CRM principles can be implemented to address the human factors-related errors and accidents occurring in the railroad industry as well (FRA, 2007). Now the safety management concept of CRM has become more and more acceptable by many industrial around the world.. 2.3.2. The Evolution of Crew Resource Management Theories The roots of crew resource management concept are thought to be traced back to a workshop, “Resource Management on the Flight-deck” sponsored by the U.S. National Aeronautics and Space Administration (NASA) in 1979 (Cooper, White, & Lauber, 1980). The first generation of CRM was originally named as the Cockpit Resource Management developed in NASA due to findings that “pilot error” was involved in the majority of air crashes. In 1980s, Cockpit Resources Management was changed to Crew Resource Management dealt with more specific aviation concepts related to flight operations and became more modular and more team oriented in nature. Basic CRM training included concepts such as team building, briefing strategies, situational awareness and stress management, while specific modules addressed decision making strategies and breaking the chain of errors that can result in catastrophe (Helmreich, Merritt & Wilhelm, 1999). In the early 1990s, the third generation of CRM began to extend its scope to include the organizational culture that determines safety. Efforts began to integrate CRM with technical training and to focus on specific skills and behaviors that pilots 14.

(26) could use to function more effectively. CRM also began to extend to other groups of personnel within airlines such as flight attendants, dispatchers, and maintenance personnel (Helmreich, Merritt & Wilhelm, 1999). For fourth generation of CRM, the focus was on integration and proceduralization for all flight crews and to integrate CRM concepts into technical training. The fifth generation of CRM was to search for a universal rationale that could be endorsed by pilots of all nations. The idea was returning to the original concept of CRM as a way to avoid error, and the conclusion was that the overarching justification for CRM should be error management (Helmreich, Merritt & Wilhelm, 1999). Built on the fifth generation’s “error management” theme, CRM has evolved to a sixth generation which further addresses the reality that flight crews must not only cope with human error inside the cockpit but also with threats to safety arising from the work environment as a whole. Therefore, CRM has been widened from “error management” to “threat and error management (TEM).” Now the CRM skills and methods are applied not only to eliminate, trap, or mitigate errors, but to identify systemic threats to safety (Kanki, Helmreich & Anca, 2010). Today, we have seen the application of CRM worldwide as a basic requirement for pilot training. Given the safety and security challenges faced by public transportation, we can expect the spread of CRM concept into critical areas of transport services to enhance operational effectiveness and reduce human errors. Through its bottom up perspectives, CRM can also change the enterprise business management culture in the future. The evolution of CRM is shown as below.. 15.

(27) Table 2: The Evolution of CRM. Generation 1st Generation CRM. Name. Key Topics of CRM. Cockpit Resource Management. Training aircraft pilot to reduce “pilot error.” Move beyond the cockpit, more. (late 1970s) 2nd Generation CRM Crew Resource Management. team oriented, and more. (early 1980s) specific to flight operations. 3rd Generation CRM Extended CRM. Extend to all personnel.. (early 1990s) Full integration and 4th Generation CRM Integrated CRM. proceduralization of CRM with. (mid to late 1990s) technical training. Two parts of EMCRM as 5th Generation CRM. Error Management Crew Resource. (1995). Management (EMCRM). human error reduction and human error containment. Threaten & error management 6th Generation CRM Threat & Error Management (TEM). to identify systemic threats to. (1999) safety.. Source: Summarized by this research.. 2.4. Platform Based Design A platform-based design is regarded as a powerful concept for coping with the increased pressure on time-to-market, design and manufacturing costs. Over the past years, many firms constantly struggle to explore cost-effective solutions to satisfy the diverse demands of customers. Therefore, the design methodology has become the focus, and design infrastructures and tools must be developed in synchrony with design methodology. The platform-based design thus becomes the objective design methodology which can trade off various components of manufacturing, non-recurring engineering (NRE) and design costs while sacrificing as little as possible potential design performance (Sangiovanni-Vincentelli, 2002; Simpson, et al, 2006). 16.

(28) Before a general understanding toward the platform-based design concept can be reached, the concept of the platform should be explored. First, a product family is a group of related products that are derived from a common set of components, modules, and/or subsystems to satisfy a variety of market niches. So the key to a successful product family is the product platform around which the product family is derived. Product platform definitions range from the set of common components, modules, or parts from which a stream of derivative products can be efficiently developed and launched to the collection of assets that are shared by a set of products (Simpson, et al, 2006; Gonzales-Zugasti, 2000). The concept of the platform has been around for years depending on the domain of application. There is also a more general definition of a platform as an abstraction layer in the design flow that facilitates a number of possible refinements. into. a. subsequent. abstraction. layer. in. the. design. flow. (Sangiovanni-Vincentelli, 2002). Based on the above definitions of the platform, the platform-based design can thus be defined as “an integration oriented design approach emphasizing systematic reuse, for developing complex products based upon platforms and compatible hardware and software virtual component, intended to reduce development risks, costs and time to market,” as defined in Taxonomies for the Development and Verification of Digital Systems (Bailey, Martin & Anderson, 2005). By the definition, the platform-based design is seen as a cost effective approach by combining both the hardware and software platforms into a system platform to meet a complex requirement. Based on the definitions given above, the following principles or basic tenets of the platform-based design can be derived to serve as a framework for product design technology research and practices (Sangiovanni-Vincentelli, 2002; Gonzales-Zugasti, 2000; Sangiovanni-Vincentelli, et al, 2004; Simpson, et al, 2006; Bailey, Martin & 17.

(29) Anderson, 2005): First, platform-based design is the creation of a stable architecture that can be rapidly extended, customized for a range of applications, and delivered to customers for quick deployment. Second, time-to-market pressure, design complexity and cost of ownership for masks are driving the industry towards more disciplined design styles that favor design reuse and correct-the- first-time implementations. Third, the identification of design is a “meeting-in-the-middle process,” where successive refinements of specifications meet with abstractions of potential implementations. Fourth, platform-base design is reusable and programmable. Reusable and programmable components guarantee a platform instance’s flexibility. Fifth, platform-base design combines hardware and software platforms into a system platform. However, due to hardware manufacturing cycles are more expensive and time-consuming, software-based implementation has become more popular and easier to modify. A competitive platform must offer a powerful software development environment. Above all, the operational efficiency and safety have been gaining more and more public attention recently. Given their wide variety of platforms used, this research intends to develop a configurable platform based CRM model to meet the various requirements for different public transportation systems. This research will first look into the characteristics of public transportations including land, air, and sea based transportation systems. Then go from the literature review of CRM concept and use such research methods as Delphi, ANP, DNP, and product design and development process to establish an applicable CRM model.. 18.

(30) Chapter 3 Research Methodology 3.1. The Analytical Process In this research, the analytical framework consists of five phases: (1) reviewing related literature on evaluating key factors; (2) establishing evaluation criteria for evaluating factors by using the Delphi method; (3) building the structure between evaluation criteria by using the DEMATEL; (4) analyzing and ranking the interrelationship among the factors by using the ANP; (5) finally, establishing a configurable CRM platform model by using the product design and development process. The analytical framework is shown as below. Figure 2: Analytical Framework.. Source: This research.. 3.2. Delphi Method The Delphi method was developed by the RAND Corporation in 1950s. It is a widely used method for gathering data from a group of experts within their domain of expertise. This technique is designed as a group communication process which aims to achieve a consensus of opinion on a specific issue. It is done by using a series of questionnaires with multiple iterations to collect data from a panel of selected subjects in order to avoid miscommunication, view domination, and generation of personality 19.

(31) conflict, hostility and other defects (Linstone & Turoff, 2002). This research uses this method to invite a group of eleven professionals currently employed in civil aviation, air rescue, and high speed rail industries with minimum five years of working experience background to provide their views and insights. The reason for this method is to identify the key elements involved for crew operations in public transportations.. 3.3. The DEMATEL Method Decision Making Trial and Evaluation Laboratory (DEMATEL) method was originally developed by the Science and Human Affairs Program of the Battelle Memorial Institute of Geneva in 1970s, with the purpose of studying the complex ‘real world problems’ dealing mainly with interactive man-model techniques and evaluating qualitative and factor-linked aspects of societal problems (Gabus & Fontela, 1972). DEMATEL technique was developed with the belief that the pioneering and proper use of scientific research methods could help to illuminate specific and intertwined phenomena and contribute to the recognition of practical solutions through a hierarchical structure. The applicability of the method is widespread, ranging from industrial planning and decision-making to urban planning and design, regional environmental assessment, analysis of world problems, and so forth. It has also been successfully applied in many situations, such as marketing strategies, control systems, safety problems, developing the competencies of global managers and group decision-making. Furthermore, a hybrid model combining the two methods has been widely used in various fields, for example, e-learning evaluation (Tzeng, Chiang & Li, 2007), airline safety measurement, and intelligent global manufacturing & logistics systems (Tzeng & Huang, 2007). Therefore, in this paper we use DEMATEL not only to detect complex relationships and build a Network Relation Map (NRM) of the criteria, but also to obtain the influence levels of each element over others. The 20.

(32) DEMATEL method is based upon graph theory, enabling us to plan and solve problems visually, so that we may divide multiple criteria into a relationship of cause and effect group, in order to better understand causal relationships. Directed graphs (also called digraphs) are more useful than directionless graphs, because digraphs will demonstrate the directed relationships of sub-systems. This method is used to analyze and form the relationship of cause and effect among evaluation criteria or to derive interrelationship among factors. It has been widely accepted as one of the best tools to solve the cause and effect relationship among the evaluation criteria (Huang et al., 2007; Chiu et al., 2006). Therefore, in this study we use DEMATEL not only to detect complex relationships and build a NRM of the criteria, but also to obtain the influence levels of each element over others; we then adopt these influence level values as the basis of the normalization supermatrix for determining ANP weights to obtain the relative importance. To apply the DEMATEL method smoothly, the authors refined the definitions based on above authors, and produced the essential definitions indicated below. The DEMATEL method is based upon graph theory, enabling us to plan and solve problems visually, so that we may divide multiple criteria into a relationship of cause and effect group, in order to better understand causal relationships. Directed graphs (also called digraphs) are more useful than directionless graphs, because digraphs will demonstrate the directed relationships of sub-systems. A digraph typically represents a communication network, or a domination relationship between individuals, etc. Suppose a system contains a set of elements, S = {s1, s2, . . . , sn}, and particular pair-wise relationships are determined for modeling, with respect to a mathematical relationship, MR. Next, portray the relationship MR as a direct-relation matrix that is indexed equally in both dimensions by elements from the set S. Then,. 21.

(33) extract the case for which the number 0 appears in the cell (i, j ), if the entry is a positive integral that has the meaning of: the ordered pair (si, sj ) is in the relationship MR; it has the kind of relationship regarding that element such that si causes element sj . The digraph portrays a contextual relationship between the elements of the system, in which a numeral represents the strength of influence (Fig. 4). The elements s1, s2, s3 and s4 represent the factors that have relationships in Fig. 2. The number between factors is influence or influenced degree. For example, an arrow from s1 to s2 represents the fact that s1 influences s2 and its influenced degree is two. The DEMATEL method can convert the relationship between the causes and effects of criteria into an intelligible structural model of the system (Chiu et al., 2006). Figure 3: An Example of the Directed Graph.. Source: Huang et al., 2011.. Definition 1 The pair-wise comparison scale may be designated as m levels, where the scores 0, 1, 2, . . . , m represent the range from ‘no influence’ to ‘very high influence’. Definition 2 The initial direct relation/influence matrix A is an n × n matrix obtained 22.

(34) by pair-wise comparisons, in terms of influences and directions between the determinants, in which a ij is denoted as the degree to which the ith determinant affects the jth determinant..  a11  a A   21   an1. a12 a22 an 2. a1n   a2n    ann . Definition 3 The normalized direct relation/influence matrix N can be obtained through (1) and (2), in which all principal diagonal elements are equal to zero. N  zA. (1). where n n   z  min1/ max  aij , 1/ max  aij , i, j  1,2,, n. i j j 1 i 1  . (2). In this case, N is called the normalized matrix. Since lim N   0. Definition 4 Then, the total relationship matrix T can be obtained using (3), where I stands for the identity matrix. T  N  N 2   N   N (I  N ) 1 , (3) where    and T is a total influence-related matrix; N is a direct influence matrix and N  [ xij ]nn ; lim ( N 2   N  ) stands for a indirect influence matrix;. [Explanation] T  N  N 2   N .  N (I  N  N 2   N 1 )(I  N )(I  N )1  N ( I  N  )(I  N )1  N (I  N )1 , when    , N   0nn. (3). where 0  xij  1 , 0   nj1 xij  1 and 0   in1 xij  1, at least one row or column of summation is equal to 1, but not all , then lim N   0nn . 23.

(35) The (i, j ) element tij of matrix T denotes the direct and indirect influences of factor i on factor j . Definition 5 The row and column sums are separately denoted as r and c within the total-relation matrix T through (4), (5), and (6). T  N  N 2   N . (4). . n. . . j 1. n1. (5).  n     tij     r  c i n1  j 1   1n. (6). r  ri n1    tij . where the r and c vectors denote the sums of the rows and columns, respectively. Definition 6 Suppose ri denotes the row sum of the ith row of matrix T . Then, ri is the sum of the influences dispatching from factor i to the other factors, both directly and indirectly. Suppose that cj denotes the column sum of the jth column of matrix T. Then, cj is the sum of the influences that factor i is receiving from the other factors. Furthermore, when i = j (i.e., the sum of the row sum and the column sum (ri + ci ) represents the index representing the strength of the influence, both dispatching and receiving), (ri + ci ) is the degree of the central role that factor i plays in the problem. If (ri −ci ) is positive, then factor i primarily is dispatching influence upon the strength of other factors; and if (ri − ci ) is negative, then factor i primarily is receiving influence from other factors (Huang et al., 2007; Liou et al., 2007; Tamura et al., 2002).. 3.4. The Analytic Network Process (ANP) Method The ANP method, a multi criteria theory of measurement developed by Saaty (1996), provides a general framework to deal with decisions without making assumptions about the independence of higher-level elements from lower level elements and about the independence of the elements within a level as in a hierarchy. 24.

(36) Compared with traditional AHP (Analytic Hierarchy Process) (Saaty, 2005) based applications (e.g. Li and Ma, 2008; Ahmad and Laplante, 2009) which usually assume the independence between criteria, ANP, a new theory that extends AHP to deal with dependence in feedback and utilizes the supermatrix approach (Saaty, 1996), is a more reasonable tool for dealing with complex MCDM problems in the real world (e.g., the selection of technology acquisition mode Lee et al., 2009, locating undesirable facilities Tuzkaya et al., 2008, the selection of logistics service provider Jharkharia and Shankar, 2007, etc.). In this section, concepts of the ANP are summarized based on Saaty’s earlier works (Saaty, 1996, 1999, 2005). The ANP is a coupling of two parts. The first consists of a control hierarchy or network of criteria and subcriteria that control the interactions. The second is a network of influences among the elements and clusters. The network varies from criterion to criterion and a different supermatrix of limiting influence is computed for each control criterion. Finally, each of these supermatrices is weighted by the priority of its control criterion and the results are synthesized through addition for all the control criteria (Saaty, 2003, 2004). A control hierarchy is a hierarchy of criteria and subcriteria for which priorities are derived in the usual way with respect to the goal of the system being considered. The criteria are used to compare the components of a system, and the sub-criteria are used to compare the elements. The criteria with respect to which influence is presented in individual super-matrices are called control criteria. Because all such influences obtained from the limits of the several super-matrices will be combined in order to obtain a measure of the priority of overall influences, the control criteria should be grouped in a structure to be used to derive priorities for them. These priorities will be used to weight the corresponding individual supermatrix limits and add. Analysis of priorities in a system can be thought of in terms of a control hierarchy 25.

(37) with dependence among its bottom-level alternatives arranged as a network as shown in Fig. 5. Dependence can occur within the components and between them. A control hierarchy at the top may be replaced by a control network with dependence among its components, which are collections of elements whose functions derive from the synergy of their interaction and hence has a higher-order function not found in any single element. The criteria in the control hierarchy that are used for comparing the components are usually the major parent criteria whose sub criteria are used to compare the elements need to be more general than those of the elements because of the greater complexity of the components. A network connects the components of a decision system. According to size, there will be a system that is made up of subsystems, with each subsystem made up of components, and each component made up of elements. The elements in each component interact or have an influence on some or all of the elements of another component with respect to a property governing the interactions of the entire system, such as energy, capital, or political influence. Figure 4 demonstrates a typical network. Those components which no arrow enters are known as source components such as C1 and C2. Those from which no arrow leaves are known as sink component such as C5. Those components which arrows both enter and exit leave are known as transient components such as C3 and C4. In addition, C3 and C4 form a cycle of two components because they feed back and forth into each other. C2 and C4 have loops that connect them to themselves and are inner dependent. All other connections represent dependence between components which are thus known to be outer dependent (Figure 5).. 26.

(38) Figure 4: The Control Hierarchy.. Source: Saaty, 1996.. Figure 5: Connections in a Network.. Source: Huang et al., 2011.. 27.

(39) A component of a decision network which was derived by the DEMATEL method in Sect. 3.2 will be denoted by Ch, h = 1, . . . , m, and assume that it has nh elements (determinants), which we denote by eh1, eh2, . . . , ehm. The influences of a given set of elements (determinants) in a component on any element in the decision system are represented by a ratio scale priority vector derived from paired comparisons of the comparative importance of one criterion and another criterion with respect to the interests or preferences of the decision makers. This relative importance value can be determined using a scale of 1–9 to represent equal importance to extreme importance (Saaty, 1996). The influence of elements (determinants) in the network on other elements (determinants) in that network can be represented in the following supermatrix: C1 e11. C1. W  C2. Cm. e11  e12  W11   e1n1   e21  W21  e22  .  .  e2 n2  .  .  . em1   . em 2   Wm1  emnm  . C2 e1n1. e21. Cm e2 n2. emnm. W1m. W12. W22 . . . . . . Wm 2. em1. . . . . . .. W2 m . . . . . . Wmm.                     . A typical entry Wij  [winx jny ] , nx {1,2,, ni } , ny {1,2,, n j } in the supermatrix, is called a block of the supermatrix in the following form where each column of Wij is a principal eigenvector of the influence of the elements (determinants) in the ith component of the network on an element (determinants) in the jth component. Some of 28.

(40) its entries may be zero corresponding to those elements (determinants) that have no influence.. Wi j.  wi1 j1   wi2 j1   .  .   wi ni j1 . wi1 j1. .. .. wi2 j2. .. .. . .. . . .. . . .. wi ni. j2. wi1 jn j   wi2 jn j   .   .  winj jn j  . After forming the supermatrix, the weighted supermatrix is derived by transforming all columns sum to unity exactly. This step is very much similar to the concept of the Markov chain in terms of ensuring that the sum of these probabilities of all states equals 1. Next, the weighted supermatrix is raised to limiting powers, such as. lim w.  . to get the global priority vector or called weights.. In addition, if the supermatrix has the effect of cyclist, the limiting supermatrix is not the only one. There are two or more limiting super-matrices in this situation, and the Cesaro sum would need to be calculated to get the priority. The weights of the kth objective being derived by using the above ANP processes, namely k , k {1,2,, n} , will be used as the weight for the kth objective in the following Section 3.6.. 3.5. DEMATEL based Network Process (DNP) Technique The DNP is the DEMATEL technique combining with ANP. The DNP is a multiple criteria decision making (MCDM) framework consisting of the DEMATEL and the ANP (See Huang, Lin & Tzeng, 2011). In this research, DEMATEL is used in conjunction with the ANP to analyze the cause and effect relationship among key 29.

(41) factors for constructing a new CRM model. The DNP process is done as shown below (Figure 6). Figure 6: DNP Process.. Source: This research.. Step 1: Gather experts’ opinions and calculate the initial direct influence matrix D. First gather experts’ opinions and calculate the direct-influence matrix by scores. In this research, a group of n experts and p factors are used for this process. Based on experts’ opinions, evaluations are made of the relationship among factors of mutual influence using a scale ranging from 1 to 5, with scores (1) representing “no influence”, (2) “low influence”, (3) “medium influence”, (4) “high influence”, and (5) “very high influence” respectively. The experts are asked to indicate the direct influence of a factor i will have on factor j, as indicated by dij. So for each expert, an p  p non-negative matrix is constructed as D( n)  [dij( n) ] , where n is the number of experts. Therefore, D(1) , 30. , D( n) are the matrices from n.

(42) experts. The initial direct influence matrix D of direct relations can thus be obtained. Step 2: Normalize the direct influence matrix based on the initial direct influence matrix D. Normalize the direct-influence matrix based on the direct-influence matrix D, the normalized direct relation matrix N is acquired by using Equation (1) n. n. N  vD; v  min{1/ max  dij ,1/ max  dij }, i, j {1, 2,..., n} i. j 1. j. i 1. (1). Step 3: Derive the total influence matrix T. Attain the total-influence matrix T. Once the normalized direct-influence matrix N is obtained, the total-influence matrix T of NRM can be obtained. T  N  N 2  ...  N k  N ( I - N )-1. (2). where k   and T is a total influence-related matrix; N is a direct influence. . matrix and N  [ xij ]nn ; lim N 2  k.  N k  stands for a indirect influence matrix and. n. 0   xij  1 or. n. , and only one. j 1. lim N k  [0]nn . The element k . x j 1. ij. n. or. x i 1. ij. equal to 1 for. . So. tij of matrix T denotes the direct and indirect influences. of factor i on factor j. Step 4: Calculate and analyze the sums of rows and columns of matrix T. Analyze the result. In this stage, the row and column sums are separately denoted as r and c within the total-relation matrix T through Equations (3), (4), and (5). T  [tij ], i, j {1,2,..., n}. (3). n  r  [ri ]n1  tij   j 1 n1. (4). 31.

(43) n  c  [c j ]1n  tij   i 1 1n. (5). where the r and c vectors denote the sums of the rows and columns, respectively. Suppose ri denotes the row sum of the i th row of matrix T . Then, ri is the sum of the influences dispatching from factor i to the other factors, both directly and indirectly. Suppose that c j denotes the column sum of the j th column of matrix T. Then, c j is the sum of the influences that factor i is receiving from the other factors. Furthermore, when i = j (i.e., the sum of the row sum and the column sum) (ri  ci ) represents the index representing the strength of the influence, both dispatching and receiving), (ri  ci ) is the degree of the central role that factor i plays in the problem. If (ri - ci ) is positive, then factor i primarily is dispatching influence upon the strength of other factors; and if (ri - ci ) is negative, then factor i primarily is receiving influence from other factors. Therefore, a causal graph can be achieved by mapping the dataset of (ri  si , ri  si ) providing a valuable approach for decision making (refer to Chiu et al., 2006; Tamura et al., 2002; Tzeng & Huang 2011). Now we call the total-influence matrix TC  tij nxn obtained by criteria and TD  tijD nxn obtained by dimensions (clusters) from TC . Then we normalize the ANP. weights of dimensions (clusters) by using influence matrix TD ..  t11D11   TD   tiD1i1    Dm1 tm1. D. t1 j1 j D. tij ij D. tmjmj. m. D t1Dm1m    d1   t1 j1 j  j 1  m m D D Dim   di   tij ij , di   tij ij , i  1,..., m tim   j 1 j 1  m  Dmj Dmm  tmm   d m   tmj j 1. 32. (6).

(44) Step 5: Obtain the supermatrix of eigenvectors from the total-influence matrix T. The original supermatrix of eigenvectors is obtained from the total-influence matrix T  [tij ] . For example, D values of the clusters in matrix TD , as Equation (5). Where if tij  D , then tijD  0 else tijD  tij , and tij is in the total-influence matrix T. The total-influence matrix TD needs to be normalized by dividing by the following formula. There, we could normalize the total-influence matrix and represent it as TD ..  t11D11 / d1   TD   tiD1i1 / di    Dm1 tm1 / d m. D. t1 j1 j / d1 D. tij ij / di D. tmjmj / d m. t1Dm1m / d1   11D11     timDim / di    iD1i1     Dmm tmm / d m  mD1m1. 1Dj. 1j. ijD. ij. mjD. mj. 1Dm  1m.   imDim    Dmm mm . (7). where ijD  tijD / di . This research adopts the normalized total-influence matrix TD ij. ij. (here after abbreviated to “the normalized matrix”) and the unweighted supermatrix W using Equation (8). It shows theses influence level values as the basis of the. normalization for determining the weighted supermatrix.  11D11  W11 21D21  W12  D12 D22  12  W21 22  W22 W*    Dji ji  Wij    D1m  W 2Dm2 m  Wm2 m1  1m. mD1  W1m  m1.   Dmi mi  Wim    Dmm mm  Wmm . (8). Step 6: Limit the weighted supermatrix to reach a long-term stable supermatrix. Limit the weighted supermatrix by raising it to a sufficiently large power k, as Equation (8), until the supermatrix has converged and become a long-term stable. 33.

(45) supermatrix to get the global priority vectors or called ANP weights .. limk (W * )k. 3.6.. VlseKriterijumska. (9). Optimizacija. I. Kompromisno. Resenje. (VIKOR) While solving a complex decision making problem by using an MCDM framework, the alternatives can be ranked and the best one can be selected based on the concept of compromise solution which was developed by Yu (1973) and Zeleny (1982). The compromise solution is a feasible solution, which is the closest to the ideal, and a compromise means an agreement established by mutual concessions (Opricovic and Tzeng, 2004). A compromise solution for a problem with conflicting criteria can help the decision makers to reach a final decision (Opricovic and Tzeng, 2004). The VIKOR method was introduced as one applicable technique to implement within MCDM (Opricovic 1998). In comparison to one of the most well-known traditional compromise ranking method, TOPSIS, which determines a solution with the shortest distance from the ideal solution and the farthest distance from the negative-ideal solution (Hwang and Yoon, 1981; Yoon, 1987), the VIKOR can the prohibit the problem of not considering the relative importance of these distances. Following, based on Opricovic and Tzeng (2004), the procedures for VIKOR are introduced as a basis for this hybrid MCDM framework. The VIKOR is applied here to derive the optimal alternative (strategy) with the shortest distance from the ideal solution. Assume the alternatives can be denoted as A1, A2, . . . , Al, . . . , Am. The rating (performance score) of the jth criterion is denoted by flj for alternative Al ,  j is the weight of the jth criterion, expressing the relative importance of the criteria, where j = 1, 2, . . . , n, and n is the number of criteria. The VIKOR method began with the 34.

(46) following form of Lp-metric:.   n Llp =   w j | f j* - flj | /   j=1. 1/ P.   f j* - f j- |    p. where 1  p   ; l = 1, 2, . . . , m; weight  j is derived using the ANP according to the NRM based on the DEMATEL method. The VIKOR method also uses. L. p1. l. (as Sl) and. L. p. l. (as Ql ) to formulate the ranking measure (Opricovic,. 1998, Tzeng et al., 2002a, 2002b; Opricovic and Tzeng, 2002, 2004, 2007; Tzeng et al., 2005; Ou Yang et al., 2009; Ho et al., 2011).. . . Sl = Llp=1 =   wj | f j* - flj | / | f j* - f j- | ,   j=1 n. Ql = Llp= = max rlj | j = 1,2, j. The compromise solution minl. L. p. l. ,n ,. will be chosen because its value is closest to. the ideal/aspired level. In addition, when p is small, the group utility is emphasized (such as p = 1) and as p increases to p=  , the individual maximal regrets/gaps receive more importance, as shown by Yu (Yu, 1973; Freimer and Yu, 1976). Therefore, minl Sl emphasizes the maximum group utility, whereas minl Ql emphasizes selecting the minimum of the maximum individual regrets. Based on the above concepts, the compromise ranking algorithm VIKOR has the following steps. Step 1: Normalize the original rating matrix. In this step, we determine the best f  j. and the worst. f.  j. values of all criterion functions, j = 1, 2, . . . , n. Assuming the j. th function represents a benefit:. f.  j. = maxl flj (or setting an aspired level) and. f.  j. =. minl flj (or setting a tolerable level). Alternatively, assuming the j th function represents a cost: f∗j= minl flj (or setting an aspired level) and fJ = maxl flj (or setting a tolerable 35.

(47) level). Moreover, an original rating matrix is transformed into a normalized weight-rating matrix with the following formula:. rlj = | f j* - flj | /  f j* - f j- | . Step 2: Compute the values Sl and Ql , l = 1, 2, . . . , m, using the. . relations Sl =  j=1 w j rlj and Ql = max rlj | j = 1,2, n. j. ,n , where Sl and Ql show the mean. of group utility and maximal regret respectively. In the traditional VIKOR method, Ql. . is represented as max w j rlj | j = 1,2, j. ,n , which implies group utility is more. important than maximal regret. Since Ql is only a part of Sl , Sl is unquestionably more than Ql . Therefore, Sl is emphasized more than Ql in the traditional VIKOR method. However, the maximal regret is also very important in practice and is usually taken into account in order to improve it. Therefore, in order to balance Sl and Ql ,. Ql = max rlj | j = 1,2, j. ,n , is used instead of the traditional VIKOR Ql .. Step 3: Compute the index values Rl , l = 1, 2, . . . , m, using the relation. Rl = v  Sl - S*  /  S - - S*  + 1- v  Ql - Q*  / Q- - Q*  ,where S * = min Sl , S - = max Sl , l. j. Q* = min Ql , Q- = max Ql (here, we can also set the best value to 0 and the worst j. j. value to 1) and 0 ≤ v ≤ 1, where v is introduced as a weight for the strategy of maximum group utility, whereas 1- v is the weight of the individual regret. Step 4: Rank the alternatives, sorting by the value of Sl , Ql and Rl , for l = 1, 2, . . . , m, in decreasing order. Propose as a compromise the alternative ( A (1)) which is ranked first by the measure minRl |l =1,2,. ,m if the following two conditions are. satisfied: C1. Acceptable advantage: R ( A (2)) − R ( A (1)) ≥ 1/(m − 1), where A (2) is 36.